Barrera-Falcón Erick, Rioja-Nieto Rodolfo, Hernández-Landa Roberto C
Posgrado en Ciencias del Mar y Limnología, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Ciudad de México, México.
Laboratorio de Análisis Espacial de Zonas Costeras (COSTALAB), Facultad de Ciencias, UMDI-Sisal, UNAM, Mérida, México.
PLoS One. 2025 Jul 23;20(7):e0318404. doi: 10.1371/journal.pone.0318404. eCollection 2025.
Understanding the structural complexity of coral reefs is essential for assessing their condition, biodiversity, and resilience. Traditional methods commonly use a rugosity index based on the chain method, which overlooks the underlying structure of coral reefs. However, digital underwater photogrammetry allows the construction of coral structure models, which can then be used to decompose reef topography across multiple layers. This study introduces a wavelet-based method for the multiscale analysis of reef structural complexity, considering reef's surface and underlying characteristics. Data were collected from six reefs within the Cozumel Reefs National Park (CRNP) at depths ranging from 6 to 14 m. High-resolution Digital Elevation Models (DEMs) and orthomosaics were constructed using digital underwater photogrammetry (UWP). The elevation profiles extracted from the DEMs were analyzed using a Maximum Overlap Discrete Wavelet Transform (MODWT), with a Daubechies mother wavelet to decompose the reef topography into local complexity (related to live coral cover) and underlying complexity (related to the historical context of the formation of the reef matrix). The wavelet-based method effectively decomposed the DEMs into components representing structural complexity at different scales, with the reconstructed DEMs statistically equivalent to the original data source (p > 0.05). The underlying reef characteristics contributed the most to the complexity estimates. Significant differences in structural complexity were observed between reefs (p < 0.05), where interpretations differed based on the contribution of the surface and underlying characteristics. For the CRNP, Agariciid and branching corals were the primary drivers of surface complexity (p < 0.05), rather than mound and boulder and meandroid corals. Our findings indicate that the chain method undervalues the historical role on assessments and the importance of local characteristics in sustaining reef structural complexity over time.
了解珊瑚礁的结构复杂性对于评估其状况、生物多样性和恢复力至关重要。传统方法通常使用基于链法的粗糙度指数，该方法忽略了珊瑚礁的底层结构。然而，数字水下摄影测量法能够构建珊瑚结构模型，进而用于跨多层分解礁体地形。本研究引入了一种基于小波的方法，用于对礁体结构复杂性进行多尺度分析，同时考虑礁体的表面和底层特征。数据收集自科苏梅尔礁国家公园（CRNP）内的六个珊瑚礁，深度范围为6至14米。使用数字水下摄影测量法（UWP）构建了高分辨率数字高程模型（DEM）和正射镶嵌图。从DEM中提取的高程剖面使用最大重叠离散小波变换（MODWT）进行分析，采用Daubechies母小波将礁体地形分解为局部复杂性（与活珊瑚覆盖有关）和底层复杂性（与礁体基质形成的历史背景有关）。基于小波的方法有效地将DEM分解为代表不同尺度结构复杂性的成分，重建后的DEM在统计上与原始数据源等效（p > 0.05）。礁体的底层特征对复杂性估计的贡献最大。在不同珊瑚礁之间观察到结构复杂性存在显著差异（p < 0.05），其解释因表面和底层特征的贡献而异。对于CRNP，鹿角珊瑚和分支珊瑚是表面复杂性的主要驱动因素（p < 0.05），而非丘状、块状和脑状珊瑚。我们的研究结果表明，链法在评估中低估了历史作用以及局部特征在长期维持礁体结构复杂性方面的重要性。
